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The Accutorr 3/Rosebud Vital Signs monitor is intended for spot-check monitoring physiologic parameters, including Pulse Oximetry (SpO2), Pulse Rate (PR), Non-Invasive Blood Pressure (NIBP) and Temperature (TEMP) on adult, pediatric, and neonatal patients in healthcare facilities by physicians or appropriate medical staff under the direction of physicians.

The subject Vital Signs Monitors includes two monitors: Accutorr 3 Vital Signs Monitor and Rosebud Vital Signs Monitor. The Vital Signs Monitors are for use for adult, pediatric, and neonatal patients. The monitors are to be used in healthcare facilities by physicians or appropriate medical staff under the direction of physicians.

This FDA 510(k) summary describes the Accutorr 3/Rosebud Vital Signs Monitor, which includes two devices: the Accutorr 3 Vital Signs Monitor and the Rosebud Vital Signs Monitor. The key change in this submission is the introduction of a new Mindray Temperature Module (TrueTemp) with updated temperature measurement capabilities.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the temperature module are based on the requirements of ISO 80601-2-56:2017 and ASTM E1112-00 for temperature accuracy. The performance data is specifically for the "Predictive mode" of the new TrueTemp module.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: The clinical accuracy study for the TrueTemp module evaluated:
  • 106 valid cases for oral temperature
  • 110 valid cases for axillary temperature
  • 109 valid cases for rectal temperature
• Data Provenance: The document does not explicitly state the country of origin. It is a prospective clinical investigation conducted to meet ISO 80601-2-56 requirements. The age of subjects ranged from 4 days to 67 years old. The study ensured that the total number of febrile subjects was not less than 30% and not greater than 50% of all subjects in the selected age group and body site.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

The document does not explicitly state the number or specific qualifications of experts used to establish ground truth for the clinical temperature study. However, the ground truth was established by comparing the device's readings to those of a WelchAllyn SureTemp PLUS 690 in direct mode, which is a legally marketed and presumably accurate reference thermometer. This implies the "experts" were the established accuracy of this reference device.

4. Adjudication Method for the Test Set

The document does not mention an explicit adjudication method (e.g., 2+1, 3+1). The "ground truth" was established by a comparative measurement against a reference device (WelchAllyn SureTemp PLUS 690 in direct mode) rather than an interpretation requiring multiple human adjudicators.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study was not done. This device is a vital signs monitor, and the clinical study focused on the accuracy of temperature measurement against a reference standard, not on human interpretation or decision-making aided by AI.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Study was done

Yes, the clinical accuracy study for the temperature module can be considered a standalone performance evaluation of the device's temperature measurement capabilities. It directly compared the device's readings to a ground truth established by a reference thermometer, without human interpretation of the device's output as the primary endpoint.

7. The Type of Ground Truth Used

The ground truth for the temperature accuracy study was established by comparison to a legally marketed and accurate reference device, specifically the WelchAllyn SureTemp PLUS 690 in direct mode. This can be categorized as a form of reference standard measurement.

8. The Sample Size for the Training Set

The document does not provide information about a training set size. This is typical for vital signs monitors, where the device's algorithms for parameters like temperature (e.g., predictive mode calculations) are developed and then validated through bench and clinical testing against established standards, rather than requiring large labeled clinical datasets for typical machine learning training.

9. How the Ground Truth for the Training Set was Established

As no training set is described, information on how its ground truth was established is not applicable here. The development of the TrueTemp module's algorithm would have involved engineering and calibration against known temperature standards and thermometry principles.

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The VS 9/VS 8/VS 8A Vital Signs Monitors are intended for monitoring, displaying, storing, alarming, and transferring of multiple physiological parameters including Temperature (Temp), Pulse Oxygen Saturation (SpO2), Pulse Rate (PR), Non-invasive Blood Pressure (NIBP), Carbon Dioxide (CO2). All the parameters can be monitored on single adult, pediatric, and neonatal patients.

The monitors are to be used in healthcare facilities by clinical professionals or under their guidance. They should only be used by persons who have received adequate training in their use. The VS 9/VS 8A Vital Signs Monitors are not intended for helicopter transport, hospital ambulance, or home use.

The subject VS series Vital Signs Monitors includes three monitors:

• VS 9 Vital Signs Monitor
• VS 8 Vital Signs Monitor
• VS 8A Vital Signs Monitor

The VS series Vital Signs Monitors are for use for adult, pediatric, and neonatal patients. The monitors are to be used in healthcare facilities by clinical professionals or under their guidance. The monitors should only be used by persons who have received adequate training in their use.

The provided text is a 510(k) summary for the Shenzhen Mindray Bio-Medical Electronics Co., LTD VS Series Vital Signs Monitors. It describes the device, its intended use, comparison to predicate devices, and the performance data submitted to demonstrate substantial equivalence.

However, the provided document does not describe an AI/ML software device or a study validating its performance. The "Product Code: MWI, CCK, DQA, DXN, FLL" and "Regulation Name: Cardiac Monitor (Including Cardiotachometer And Rate Alarm)" indicate traditional vital signs monitors. The 'software verification and validation testing' mentioned refers to general software engineering practices for medical devices, not specific performance criteria for an AI/ML algorithm.

Therefore, I cannot provide the requested information because the document does not contain details about:

• Acceptance criteria for an AI/ML algorithm.
• A study proving an AI/ML device meets acceptance criteria.
• Sample sizes for test sets used for AI/ML validation.
• Ground truth establishment methods for AI/ML datasets.
• MRMC studies for AI/ML.
• Standalone performance for AI/ML algorithms.
• Training set details for AI/ML.

The document discusses validation for a physical medical device (vital signs monitor) including:

• Biocompatibility Testing: For NIBP cuffs due to an ink change.
• Software Verification and Validation Testing: General software V&V for the device's embedded software, not an AI/ML algorithm.
• Electrical safety and electromagnetic compatibility (EMC): Compliance with relevant standards.
• Bench Testing: Functional and system level testing to validate device performance.
• Clinical Data: NIBP measurement accuracy validated according to ISO 81060-2:2018 in neonate, infant, child, and adult populations.

If you have a document describing an AI/ML medical device, please provide that, and I will be able to answer your questions.

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The monitors are intended to be used for monitoring, storing, recording of, and to generate alarms for, multiple physiological parameters of adults, pediatrics and neonates. The monitors are intended for use by trained healthcare professionals in hospital environments.

The monitored physiological parameters include: ECG, respiration (RESP), temperature (TEMP), oxygen saturation of arterial blood (SpO2), pulse rate (PR), non-invasive blood pressure (NIBP), invasive blood pressure (IBP), carbon dioxide (CO2), cardiac output (C.O.).

The arrhythmia detection and ST Segment analysis are intended for adult patients. The monitors are not intended for MRI environments.

The X8 X10 X12 Patient Monitor (hereinafter called X series) can perform long-time continuous monitoring of multiple physiological parameters. Also, it is capable of storing, displaying, analyzing and controlling measurements, and it will indicate alarms in case of abnormity so that doctors and nurses can deal with them in time.

The provided text is a 510(k) Premarket Notification summary for the Edan Instruments, Inc. Patient Monitor (Models X8, X10, X12). This document focuses on demonstrating substantial equivalence to a predicate device, rather than proving the device meets specific acceptance criteria through a clinical study of an AI algorithm's performance.

Therefore, the information required to answer the prompt (acceptance criteria, study details for an AI algorithm, expert ground truth, MRMC study, standalone performance, training set details) is not present in the provided document. The document describes a traditional medical device (patient monitor) and its compliance with general safety and performance standards.

Here's a breakdown of why the requested information cannot be extracted from the given text:

1. Table of acceptance criteria and reported device performance: The document lists various physiological parameters (ECG, RESP, NIBP, SpO2, Temp, IBP, CO2, C.O.) and their measurement ranges and accuracies, comparing them to a predicate device. These are performance specifications, not "acceptance criteria" in the context of an AI algorithm's performance study. There's no specific numerical performance target for an AI algorithm provided.

2. Sample size used for the test set and the data provenance: Not applicable. The document refers to "bench testing" and "software verification and validation testing," which are not described as using specific test sets of patient data for AI algorithm evaluation.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth establishment by experts is relevant for AI algorithm studies, which are not detailed here.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable. This pertains to expert review in AI studies, which is not described.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not applicable. No MRMC study is mentioned. This device is a patient monitor, not an AI-assisted diagnostic tool for human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. No standalone algorithm performance is described. The device's "arrhythmia detection and ST Segment analysis" are features of the patient monitor, not explicitly a separate AI algorithm being evaluated for standalone performance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable. The "ground truth" for the device's measurements (e.g., blood pressure accuracy) would be derived from calibrated reference instruments, not expert consensus or pathology in the context of an AI algorithm.

8. The sample size for the training set: Not applicable. There is no mention of a training set for an AI algorithm.

9. How the ground truth for the training set was established: Not applicable. There is no mention of a training set for an AI algorithm.

In summary, the provided FDA 510(k) summary for the Edan Instruments, Inc. Patient Monitor focuses on establishing substantial equivalence to existing predicate devices based on design features, performance specifications (e.g., accuracy of physiological measurements), and compliance with electrical safety and EMC standards. It does not describe a study involving an AI algorithm that would require the detailed information requested in the prompt.

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